Overview

Dataset statistics

Number of variables19
Number of observations5097
Missing cells44969
Missing cells (%)46.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory756.7 KiB
Average record size in memory152.0 B

Variable types

Text1
Numeric13
Unsupported5

Alerts

population is highly overall correlated with population_male and 10 other fieldsHigh correlation
population_male is highly overall correlated with population and 10 other fieldsHigh correlation
population_female is highly overall correlated with population and 10 other fieldsHigh correlation
population_age_00_09 is highly overall correlated with population and 10 other fieldsHigh correlation
population_age_10_19 is highly overall correlated with population and 10 other fieldsHigh correlation
population_age_20_29 is highly overall correlated with population and 10 other fieldsHigh correlation
population_age_30_39 is highly overall correlated with population and 10 other fieldsHigh correlation
population_age_40_49 is highly overall correlated with population and 10 other fieldsHigh correlation
population_age_50_59 is highly overall correlated with population and 10 other fieldsHigh correlation
population_age_60_69 is highly overall correlated with population and 10 other fieldsHigh correlation
population_age_70_79 is highly overall correlated with population and 10 other fieldsHigh correlation
population_age_80_and_older is highly overall correlated with population and 10 other fieldsHigh correlation
population_male has 1354 (26.6%) missing valuesMissing
population_female has 1354 (26.6%) missing valuesMissing
population_rural has 5097 (100.0%) missing valuesMissing
population_urban has 5097 (100.0%) missing valuesMissing
population_largest_city has 5097 (100.0%) missing valuesMissing
population_clustered has 5097 (100.0%) missing valuesMissing
population_density has 4590 (90.1%) missing valuesMissing
human_development_index has 5097 (100.0%) missing valuesMissing
population_age_00_09 has 1354 (26.6%) missing valuesMissing
population_age_10_19 has 1354 (26.6%) missing valuesMissing
population_age_20_29 has 1354 (26.6%) missing valuesMissing
population_age_30_39 has 1354 (26.6%) missing valuesMissing
population_age_40_49 has 1354 (26.6%) missing valuesMissing
population_age_50_59 has 1354 (26.6%) missing valuesMissing
population_age_60_69 has 1354 (26.6%) missing valuesMissing
population_age_70_79 has 1354 (26.6%) missing valuesMissing
population_age_80_and_older has 1354 (26.6%) missing valuesMissing
location_key has unique valuesUnique
population_rural is an unsupported type, check if it needs cleaning or further analysisUnsupported
population_urban is an unsupported type, check if it needs cleaning or further analysisUnsupported
population_largest_city is an unsupported type, check if it needs cleaning or further analysisUnsupported
population_clustered is an unsupported type, check if it needs cleaning or further analysisUnsupported
human_development_index is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-09-07 23:59:38.344641
Analysis finished2023-09-07 23:59:52.745786
Duration14.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

location_key
Text

UNIQUE 

Distinct5097
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size39.9 KiB
2023-09-08T01:59:52.840827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.935845
Min length8

Characters and Unicode

Total characters55740
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5097 ?
Unique (%)100.0%

Sample

1st rowDE_BB_12051
2nd rowDE_BB_12052
3rd rowDE_BB_12053
4th rowDE_BB_12054
5th rowDE_BB_12060
ValueCountFrequency (%)
de_bb_12051 1
 
< 0.1%
de_bb_12067 1
 
< 0.1%
de_bb_12060 1
 
< 0.1%
de_bb_12061 1
 
< 0.1%
de_bb_12062 1
 
< 0.1%
de_bb_12063 1
 
< 0.1%
de_bb_12064 1
 
< 0.1%
de_be_11002 1
 
< 0.1%
de_bb_12065 1
 
< 0.1%
de_bb_12068 1
 
< 0.1%
Other values (5087) 5087
99.8%
2023-09-08T01:59:53.071532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 10194
18.3%
S 4945
 
8.9%
0 4799
 
8.6%
1 4516
 
8.1%
U 3262
 
5.9%
2 2794
 
5.0%
3 2602
 
4.7%
5 2299
 
4.1%
7 2028
 
3.6%
E 1931
 
3.5%
Other values (27) 16370
29.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25122
45.1%
Uppercase Letter 20424
36.6%
Connector Punctuation 10194
18.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 4945
24.2%
U 3262
16.0%
E 1931
 
9.5%
T 1693
 
8.3%
C 1477
 
7.2%
N 882
 
4.3%
A 840
 
4.1%
D 785
 
3.8%
M 710
 
3.5%
I 661
 
3.2%
Other values (16) 3238
15.9%
Decimal Number
ValueCountFrequency (%)
0 4799
19.1%
1 4516
18.0%
2 2794
11.1%
3 2602
10.4%
5 2299
9.2%
7 2028
8.1%
4 1765
 
7.0%
8 1689
 
6.7%
9 1559
 
6.2%
6 1071
 
4.3%
Connector Punctuation
ValueCountFrequency (%)
_ 10194
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 35316
63.4%
Latin 20424
36.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 4945
24.2%
U 3262
16.0%
E 1931
 
9.5%
T 1693
 
8.3%
C 1477
 
7.2%
N 882
 
4.3%
A 840
 
4.1%
D 785
 
3.8%
M 710
 
3.5%
I 661
 
3.2%
Other values (16) 3238
15.9%
Common
ValueCountFrequency (%)
_ 10194
28.9%
0 4799
13.6%
1 4516
12.8%
2 2794
 
7.9%
3 2602
 
7.4%
5 2299
 
6.5%
7 2028
 
5.7%
4 1765
 
5.0%
8 1689
 
4.8%
9 1559
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55740
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 10194
18.3%
S 4945
 
8.9%
0 4799
 
8.6%
1 4516
 
8.1%
U 3262
 
5.9%
2 2794
 
5.0%
3 2602
 
4.7%
5 2299
 
4.1%
7 2028
 
3.6%
E 1931
 
3.5%
Other values (27) 16370
29.4%

population
Real number (ℝ)

HIGH CORRELATION 

Distinct4779
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98227.802
Minimum22
Maximum10103711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.9 KiB
2023-09-08T01:59:53.192614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile247.8
Q14872
median20092
Q372816
95-th percentile403104
Maximum10103711
Range10103689
Interquartile range (IQR)67944

Descriptive statistics

Standard deviation321366.25
Coefficient of variation (CV)3.2716425
Kurtosis320.85824
Mean98227.802
Median Absolute Deviation (MAD)18845
Skewness14.083007
Sum5.0066711 × 108
Variance1.0327627 × 1011
MonotonicityNot monotonic
2023-09-08T01:59:53.309204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
145 5
 
0.1%
158 5
 
0.1%
187 5
 
0.1%
167 4
 
0.1%
179 4
 
0.1%
92 4
 
0.1%
357 4
 
0.1%
196 4
 
0.1%
244 4
 
0.1%
169 4
 
0.1%
Other values (4769) 5054
99.2%
ValueCountFrequency (%)
22 1
 
< 0.1%
27 1
 
< 0.1%
28 1
 
< 0.1%
36 1
 
< 0.1%
41 1
 
< 0.1%
42 1
 
< 0.1%
46 3
0.1%
48 1
 
< 0.1%
49 2
< 0.1%
51 1
 
< 0.1%
ValueCountFrequency (%)
10103711 1
< 0.1%
8437478 1
< 0.1%
5200821 1
< 0.1%
4657972 1
< 0.1%
4342212 1
< 0.1%
4327184 1
< 0.1%
3460949 1
< 0.1%
3321237 1
< 0.1%
3305408 1
< 0.1%
3250315 1
< 0.1%

population_male
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3626
Distinct (%)96.9%
Missing1354
Missing (%)26.6%
Infinite0
Infinite (%)0.0%
Mean62701.48
Minimum41
Maximum4980981
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.9 KiB
2023-09-08T01:59:53.507900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile1487.1
Q16235.5
median16619
Q355830.5
95-th percentile255700.6
Maximum4980981
Range4980940
Interquartile range (IQR)49595

Descriptive statistics

Standard deviation177826.1
Coefficient of variation (CV)2.836075
Kurtosis258.28756
Mean62701.48
Median Absolute Deviation (MAD)12983
Skewness12.689533
Sum2.3469164 × 108
Variance3.1622122 × 1010
MonotonicityNot monotonic
2023-09-08T01:59:53.603877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5741 3
 
0.1%
10997 3
 
0.1%
3643 3
 
0.1%
4799 3
 
0.1%
5513 3
 
0.1%
8114 3
 
0.1%
7408 2
 
< 0.1%
5386 2
 
< 0.1%
2157 2
 
< 0.1%
1007 2
 
< 0.1%
Other values (3616) 3717
72.9%
(Missing) 1354
 
26.6%
ValueCountFrequency (%)
41 1
< 0.1%
70 1
< 0.1%
144 1
< 0.1%
222 1
< 0.1%
225 1
< 0.1%
242 1
< 0.1%
246 1
< 0.1%
277 1
< 0.1%
317 1
< 0.1%
318 1
< 0.1%
ValueCountFrequency (%)
4980981 1
< 0.1%
4025585 1
< 0.1%
2524876 1
< 0.1%
2314349 1
< 0.1%
2140106 1
< 0.1%
2081239 1
< 0.1%
1672189 1
< 0.1%
1670689 1
< 0.1%
1576316 1
< 0.1%
1567002 1
< 0.1%

population_female
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3590
Distinct (%)95.9%
Missing1354
Missing (%)26.6%
Infinite0
Infinite (%)0.0%
Mean64829.592
Minimum45
Maximum5122730
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.9 KiB
2023-09-08T01:59:53.698711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile1482.9
Q16166.5
median16701
Q356912.5
95-th percentile269557.6
Maximum5122730
Range5122685
Interquartile range (IQR)50746

Descriptive statistics

Standard deviation186375.23
Coefficient of variation (CV)2.8748482
Kurtosis259.53298
Mean64829.592
Median Absolute Deviation (MAD)13266
Skewness12.764398
Sum2.4265716 × 108
Variance3.4735728 × 1010
MonotonicityNot monotonic
2023-09-08T01:59:53.795072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2116 4
 
0.1%
1545 3
 
0.1%
5505 3
 
0.1%
1010 3
 
0.1%
2393 3
 
0.1%
21895 2
 
< 0.1%
2841 2
 
< 0.1%
8111 2
 
< 0.1%
1770 2
 
< 0.1%
407 2
 
< 0.1%
Other values (3580) 3717
72.9%
(Missing) 1354
 
26.6%
ValueCountFrequency (%)
45 1
< 0.1%
63 1
< 0.1%
145 1
< 0.1%
205 1
< 0.1%
230 1
< 0.1%
235 1
< 0.1%
237 1
< 0.1%
247 1
< 0.1%
270 1
< 0.1%
287 1
< 0.1%
ValueCountFrequency (%)
5122730 1
< 0.1%
4411893 1
< 0.1%
2675945 1
< 0.1%
2343623 1
< 0.1%
2260973 1
< 0.1%
2187078 1
< 0.1%
1790260 1
< 0.1%
1673999 1
< 0.1%
1649048 1
< 0.1%
1607287 1
< 0.1%

population_rural
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing5097
Missing (%)100.0%
Memory size39.9 KiB

population_urban
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing5097
Missing (%)100.0%
Memory size39.9 KiB

population_largest_city
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing5097
Missing (%)100.0%
Memory size39.9 KiB

population_clustered
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing5097
Missing (%)100.0%
Memory size39.9 KiB

population_density
Real number (ℝ)

MISSING 

Distinct488
Distinct (%)96.3%
Missing4590
Missing (%)90.1%
Infinite0
Infinite (%)0.0%
Mean476.74004
Minimum36.2
Maximum4721.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.9 KiB
2023-09-08T01:59:53.900292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.2
5-th percentile63.93
Q1116.45
median192.2
Q3499.2
95-th percentile1998.89
Maximum4721.9
Range4685.7
Interquartile range (IQR)382.75

Descriptive statistics

Standard deviation644.21986
Coefficient of variation (CV)1.3513022
Kurtosis7.1431019
Mean476.74004
Median Absolute Deviation (MAD)101.3
Skewness2.4958858
Sum241707.2
Variance415019.23
MonotonicityNot monotonic
2023-09-08T01:59:53.998883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102 3
 
0.1%
117.6 3
 
0.1%
140 2
 
< 0.1%
222.3 2
 
< 0.1%
145.4 2
 
< 0.1%
107.2 2
 
< 0.1%
94.5 2
 
< 0.1%
55.9 2
 
< 0.1%
101.5 2
 
< 0.1%
66.4 2
 
< 0.1%
Other values (478) 485
 
9.5%
(Missing) 4590
90.1%
ValueCountFrequency (%)
36.2 1
< 0.1%
36.8 1
< 0.1%
39.3 1
< 0.1%
39.8 1
< 0.1%
40.1 1
< 0.1%
40.2 1
< 0.1%
41.5 1
< 0.1%
45.6 1
< 0.1%
47.1 1
< 0.1%
50 1
< 0.1%
ValueCountFrequency (%)
4721.9 1
< 0.1%
3076.6 1
< 0.1%
3067.3 1
< 0.1%
3061.1 1
< 0.1%
3001.4 1
< 0.1%
2902.3 1
< 0.1%
2817.6 1
< 0.1%
2793.4 1
< 0.1%
2791.3 1
< 0.1%
2741.9 1
< 0.1%

human_development_index
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing5097
Missing (%)100.0%
Memory size39.9 KiB

population_age_00_09
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3261
Distinct (%)87.1%
Missing1354
Missing (%)26.6%
Infinite0
Infinite (%)0.0%
Mean14437.204
Minimum0
Maximum1228873
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.9 KiB
2023-09-08T01:59:54.104049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile341.2
Q11447.5
median3911
Q312074.5
95-th percentile56842.7
Maximum1228873
Range1228873
Interquartile range (IQR)10627

Descriptive statistics

Standard deviation44236.776
Coefficient of variation (CV)3.0640819
Kurtosis272.59862
Mean14437.204
Median Absolute Deviation (MAD)3061
Skewness13.339309
Sum54038455
Variance1.9568923 × 109
MonotonicityNot monotonic
2023-09-08T01:59:54.212359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
796 5
 
0.1%
1402 4
 
0.1%
1566 4
 
0.1%
1339 4
 
0.1%
339 4
 
0.1%
1240 4
 
0.1%
302 4
 
0.1%
375 4
 
0.1%
1317 4
 
0.1%
198 3
 
0.1%
Other values (3251) 3703
72.7%
(Missing) 1354
 
26.6%
ValueCountFrequency (%)
0 1
< 0.1%
24 1
< 0.1%
34 1
< 0.1%
40 1
< 0.1%
46 1
< 0.1%
53 1
< 0.1%
54 1
< 0.1%
55 1
< 0.1%
57 1
< 0.1%
58 1
< 0.1%
ValueCountFrequency (%)
1228873 1
< 0.1%
1033227 1
< 0.1%
706703 1
< 0.1%
638270 1
< 0.1%
568976 1
< 0.1%
455082 1
< 0.1%
410986 1
< 0.1%
390959 1
< 0.1%
379592 1
< 0.1%
379364 1
< 0.1%

population_age_10_19
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3285
Distinct (%)87.8%
Missing1354
Missing (%)26.6%
Infinite0
Infinite (%)0.0%
Mean15078.247
Minimum0
Maximum1252274
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.9 KiB
2023-09-08T01:59:54.312336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile372.2
Q11542
median4146
Q313013.5
95-th percentile59598.5
Maximum1252274
Range1252274
Interquartile range (IQR)11471.5

Descriptive statistics

Standard deviation43853.067
Coefficient of variation (CV)2.9083665
Kurtosis258.78743
Mean15078.247
Median Absolute Deviation (MAD)3272
Skewness12.773757
Sum56437877
Variance1.9230915 × 109
MonotonicityNot monotonic
2023-09-08T01:59:54.416739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3550 4
 
0.1%
392 4
 
0.1%
757 4
 
0.1%
1270 4
 
0.1%
836 4
 
0.1%
834 4
 
0.1%
2081 4
 
0.1%
1409 3
 
0.1%
2570 3
 
0.1%
2523 3
 
0.1%
Other values (3275) 3706
72.7%
(Missing) 1354
 
26.6%
ValueCountFrequency (%)
0 1
< 0.1%
25 1
< 0.1%
45 1
< 0.1%
46 1
< 0.1%
51 1
< 0.1%
52 1
< 0.1%
55 1
< 0.1%
57 2
< 0.1%
61 1
< 0.1%
63 1
< 0.1%
ValueCountFrequency (%)
1252274 1
< 0.1%
903623 1
< 0.1%
662619 1
< 0.1%
629856 1
< 0.1%
591374 1
< 0.1%
472818 1
< 0.1%
408705 1
< 0.1%
407689 1
< 0.1%
403031 1
< 0.1%
368330 1
< 0.1%

population_age_20_29
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3250
Distinct (%)86.8%
Missing1354
Missing (%)26.6%
Infinite0
Infinite (%)0.0%
Mean16786.025
Minimum1
Maximum1576001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.9 KiB
2023-09-08T01:59:54.516959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile294.5
Q11405
median4025
Q313959.5
95-th percentile65944.5
Maximum1576001
Range1576000
Interquartile range (IQR)12554.5

Descriptive statistics

Standard deviation53160.21
Coefficient of variation (CV)3.1669326
Kurtosis332.55792
Mean16786.025
Median Absolute Deviation (MAD)3278
Skewness14.598427
Sum62830091
Variance2.8260079 × 109
MonotonicityNot monotonic
2023-09-08T01:59:54.640800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
344 5
 
0.1%
685 5
 
0.1%
997 5
 
0.1%
185 4
 
0.1%
877 4
 
0.1%
1501 4
 
0.1%
589 4
 
0.1%
335 4
 
0.1%
1111 4
 
0.1%
138 4
 
0.1%
Other values (3240) 3700
72.6%
(Missing) 1354
 
26.6%
ValueCountFrequency (%)
1 1
< 0.1%
25 1
< 0.1%
26 1
< 0.1%
36 1
< 0.1%
40 1
< 0.1%
42 1
< 0.1%
43 1
< 0.1%
46 1
< 0.1%
48 1
< 0.1%
49 1
< 0.1%
ValueCountFrequency (%)
1576001 1
< 0.1%
1335914 1
< 0.1%
778618 1
< 0.1%
699611 1
< 0.1%
615607 1
< 0.1%
543614 1
< 0.1%
503162 1
< 0.1%
456526 1
< 0.1%
421150 1
< 0.1%
410536 1
< 0.1%

population_age_30_39
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3233
Distinct (%)86.4%
Missing1354
Missing (%)26.6%
Infinite0
Infinite (%)0.0%
Mean16638.446
Minimum10
Maximum1484454
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.9 KiB
2023-09-08T01:59:54.757102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile319
Q11408.5
median3868
Q313662.5
95-th percentile66578.1
Maximum1484454
Range1484444
Interquartile range (IQR)12254

Descriptive statistics

Standard deviation52834.043
Coefficient of variation (CV)3.1754192
Kurtosis298.70325
Mean16638.446
Median Absolute Deviation (MAD)3110
Skewness13.884594
Sum62277705
Variance2.7914361 × 109
MonotonicityNot monotonic
2023-09-08T01:59:54.872421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
831 5
 
0.1%
114 5
 
0.1%
6734 4
 
0.1%
770 4
 
0.1%
2053 4
 
0.1%
756 4
 
0.1%
1321 4
 
0.1%
665 4
 
0.1%
807 4
 
0.1%
170 4
 
0.1%
Other values (3223) 3701
72.6%
(Missing) 1354
 
26.6%
ValueCountFrequency (%)
10 1
< 0.1%
15 1
< 0.1%
38 2
< 0.1%
42 1
< 0.1%
44 1
< 0.1%
54 1
< 0.1%
57 2
< 0.1%
58 1
< 0.1%
66 1
< 0.1%
69 2
< 0.1%
ValueCountFrequency (%)
1484454 1
< 0.1%
1333694 1
< 0.1%
784213 1
< 0.1%
723165 1
< 0.1%
593034 1
< 0.1%
528328 1
< 0.1%
513822 1
< 0.1%
494483 1
< 0.1%
423754 1
< 0.1%
422471 1
< 0.1%

population_age_40_49
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3285
Distinct (%)87.8%
Missing1354
Missing (%)26.6%
Infinite0
Infinite (%)0.0%
Mean16415.35
Minimum6
Maximum1362194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.9 KiB
2023-09-08T01:59:54.991498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile314.1
Q11461.5
median3895
Q313567.5
95-th percentile66687.2
Maximum1362194
Range1362188
Interquartile range (IQR)12106

Descriptive statistics

Standard deviation49003.89
Coefficient of variation (CV)2.985248
Kurtosis250.44352
Mean16415.35
Median Absolute Deviation (MAD)3101
Skewness12.585596
Sum61442654
Variance2.4013812 × 109
MonotonicityNot monotonic
2023-09-08T01:59:55.114730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
619 5
 
0.1%
946 4
 
0.1%
199 4
 
0.1%
424 4
 
0.1%
928 4
 
0.1%
233 4
 
0.1%
2446 4
 
0.1%
3808 4
 
0.1%
637 4
 
0.1%
1087 4
 
0.1%
Other values (3275) 3702
72.6%
(Missing) 1354
 
26.6%
ValueCountFrequency (%)
6 1
< 0.1%
9 1
< 0.1%
26 1
< 0.1%
37 1
< 0.1%
42 1
< 0.1%
43 1
< 0.1%
46 2
< 0.1%
48 1
< 0.1%
50 1
< 0.1%
52 2
< 0.1%
ValueCountFrequency (%)
1362194 1
< 0.1%
1074408 1
< 0.1%
712014 1
< 0.1%
662006 1
< 0.1%
617613 1
< 0.1%
554112 1
< 0.1%
519672 1
< 0.1%
494881 1
< 0.1%
432058 1
< 0.1%
416917 1
< 0.1%

population_age_50_59
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3324
Distinct (%)88.8%
Missing1354
Missing (%)26.6%
Infinite0
Infinite (%)0.0%
Mean17949.091
Minimum15
Maximum1309380
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.9 KiB
2023-09-08T01:59:55.230228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile413
Q11743
median4600
Q316209
95-th percentile73720
Maximum1309380
Range1309365
Interquartile range (IQR)14466

Descriptive statistics

Standard deviation48527.18
Coefficient of variation (CV)2.7036009
Kurtosis227.25751
Mean17949.091
Median Absolute Deviation (MAD)3620
Skewness11.7921
Sum67183449
Variance2.3548872 × 109
MonotonicityNot monotonic
2023-09-08T01:59:55.350347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1220 5
 
0.1%
486 4
 
0.1%
295 4
 
0.1%
332 4
 
0.1%
246 4
 
0.1%
2245 3
 
0.1%
910 3
 
0.1%
346 3
 
0.1%
1079 3
 
0.1%
1085 3
 
0.1%
Other values (3314) 3707
72.7%
(Missing) 1354
 
26.6%
ValueCountFrequency (%)
15 1
< 0.1%
20 1
< 0.1%
42 1
< 0.1%
52 2
< 0.1%
71 1
< 0.1%
78 1
< 0.1%
79 1
< 0.1%
81 1
< 0.1%
84 1
< 0.1%
86 1
< 0.1%
ValueCountFrequency (%)
1309380 1
< 0.1%
1061288 1
< 0.1%
695965 1
< 0.1%
664612 1
< 0.1%
546527 1
< 0.1%
529939 1
< 0.1%
499331 1
< 0.1%
455071 1
< 0.1%
439615 1
< 0.1%
414508 1
< 0.1%

population_age_60_69
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3301
Distinct (%)88.2%
Missing1354
Missing (%)26.6%
Infinite0
Infinite (%)0.0%
Mean14751.427
Minimum16
Maximum992699
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.9 KiB
2023-09-08T01:59:55.469490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile414.1
Q11674
median4258
Q313951
95-th percentile59012
Maximum992699
Range992683
Interquartile range (IQR)12277

Descriptive statistics

Standard deviation37887.495
Coefficient of variation (CV)2.5683952
Kurtosis220.39056
Mean14751.427
Median Absolute Deviation (MAD)3301
Skewness11.56785
Sum55214592
Variance1.4354623 × 109
MonotonicityNot monotonic
2023-09-08T01:59:55.702590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2694 4
 
0.1%
1602 4
 
0.1%
531 4
 
0.1%
481 4
 
0.1%
1125 3
 
0.1%
1180 3
 
0.1%
3087 3
 
0.1%
247 3
 
0.1%
3540 3
 
0.1%
2203 3
 
0.1%
Other values (3291) 3709
72.8%
(Missing) 1354
 
26.6%
ValueCountFrequency (%)
16 1
< 0.1%
22 1
< 0.1%
31 1
< 0.1%
41 1
< 0.1%
60 1
< 0.1%
75 1
< 0.1%
78 1
< 0.1%
83 1
< 0.1%
90 1
< 0.1%
93 1
< 0.1%
ValueCountFrequency (%)
992699 1
< 0.1%
864991 1
< 0.1%
547237 1
< 0.1%
501458 1
< 0.1%
439605 1
< 0.1%
404624 1
< 0.1%
364488 1
< 0.1%
334581 1
< 0.1%
326790 1
< 0.1%
325350 1
< 0.1%

population_age_70_79
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3071
Distinct (%)82.0%
Missing1354
Missing (%)26.6%
Infinite0
Infinite (%)0.0%
Mean9495.7481
Minimum7
Maximum548702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.9 KiB
2023-09-08T01:59:55.799946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile248.1
Q11044
median2621
Q38564
95-th percentile38972.5
Maximum548702
Range548695
Interquartile range (IQR)7520

Descriptive statistics

Standard deviation23815.958
Coefficient of variation (CV)2.5080655
Kurtosis169.1151
Mean9495.7481
Median Absolute Deviation (MAD)2056
Skewness10.271172
Sum35542585
Variance5.6719986 × 108
MonotonicityNot monotonic
2023-09-08T01:59:55.898847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
268 6
 
0.1%
222 4
 
0.1%
1643 4
 
0.1%
235 4
 
0.1%
1698 4
 
0.1%
2583 4
 
0.1%
200 4
 
0.1%
952 4
 
0.1%
242 4
 
0.1%
350 4
 
0.1%
Other values (3061) 3701
72.6%
(Missing) 1354
 
26.6%
ValueCountFrequency (%)
7 2
< 0.1%
22 1
< 0.1%
38 1
< 0.1%
39 1
< 0.1%
40 1
< 0.1%
47 1
< 0.1%
48 1
< 0.1%
50 1
< 0.1%
54 1
< 0.1%
57 1
< 0.1%
ValueCountFrequency (%)
548702 1
< 0.1%
505978 1
< 0.1%
406123 1
< 0.1%
324842 1
< 0.1%
303780 1
< 0.1%
282388 1
< 0.1%
250534 1
< 0.1%
198005 1
< 0.1%
192397 1
< 0.1%
186337 1
< 0.1%

population_age_80_and_older
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2713
Distinct (%)72.5%
Missing1354
Missing (%)26.6%
Infinite0
Infinite (%)0.0%
Mean5979.5357
Minimum0
Maximum349134
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.9 KiB
2023-09-08T01:59:56.004452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile170.1
Q1603
median1463
Q35087.5
95-th percentile25601.8
Maximum349134
Range349134
Interquartile range (IQR)4484.5

Descriptive statistics

Standard deviation15566.849
Coefficient of variation (CV)2.6033541
Kurtosis168.82486
Mean5979.5357
Median Absolute Deviation (MAD)1115
Skewness10.303967
Sum22381402
Variance2.4232677 × 108
MonotonicityNot monotonic
2023-09-08T01:59:56.110444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
406 8
 
0.2%
382 6
 
0.1%
870 6
 
0.1%
203 6
 
0.1%
288 6
 
0.1%
724 5
 
0.1%
795 5
 
0.1%
183 5
 
0.1%
405 5
 
0.1%
823 5
 
0.1%
Other values (2703) 3686
72.3%
(Missing) 1354
 
26.6%
ValueCountFrequency (%)
0 1
 
< 0.1%
15 1
 
< 0.1%
17 1
 
< 0.1%
20 2
< 0.1%
24 2
< 0.1%
25 1
 
< 0.1%
27 3
0.1%
29 2
< 0.1%
30 1
 
< 0.1%
32 3
0.1%
ValueCountFrequency (%)
349134 1
< 0.1%
324355 1
< 0.1%
288877 1
< 0.1%
236261 1
< 0.1%
192229 1
< 0.1%
183288 1
< 0.1%
152149 1
< 0.1%
119974 1
< 0.1%
117379 1
< 0.1%
116780 1
< 0.1%

Interactions

2023-09-08T01:59:51.081722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:38.779942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:39.837505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:40.764366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:41.715449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:42.642383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:43.675678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:44.607761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:45.625403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:46.692873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:47.711055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:48.762150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:49.818465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:51.160471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:38.852440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:39.906319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:40.832218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:41.774711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:42.712034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:43.744881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:44.677529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:45.700669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:46.776640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:47.791561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:48.831743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:49.885171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:51.247066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:38.930363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:39.977063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:40.898880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:41.835560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:42.877234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:43.813844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:44.748867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:45.768537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:46.849775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:47.868799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:48.898684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:49.952041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:51.322111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:39.005530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:40.041052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:40.963681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:41.904978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:42.942094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:43.882169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:44.817122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:45.836455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:46.922428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:47.945445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:48.962572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:50.043680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:51.396014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:39.075682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:40.102746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:41.023918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:41.977534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:43.003035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:43.944468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:44.875395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:45.906183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:46.989798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:48.021990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:49.030859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:50.140553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:51.478727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:39.152655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:40.177521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:41.109165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:42.049619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:43.079602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:44.015025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:44.951889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:45.978949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:47.067765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:48.107674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:49.115089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:50.271783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:51.564551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:39.226142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:40.244837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:41.189394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:42.148263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:43.149345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:44.087657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:45.028935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:46.154490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:47.144609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:48.194494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:49.199651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:50.430985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:51.690064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:39.383482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:40.323478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:41.273839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:42.214015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:43.222217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:44.164420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:45.119309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:46.227284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:47.223752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:48.285790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:49.277477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:50.591987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:51.787839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:39.462110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:40.397942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:41.349489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:42.292280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:43.293549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:44.237769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:45.205723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:46.297185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:47.299357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:48.373563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:49.354831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:50.680256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:51.871224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:39.537942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:40.473551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:41.432041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:42.366285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:43.367997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:44.312754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:45.292382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:46.369078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:47.380419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:48.465905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:49.434832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:50.754879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:51.949592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:39.613671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:40.542546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:41.502427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:42.441814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:43.438001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:44.384045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:45.376551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:46.453849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:47.454708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:48.545774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:49.508133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:50.822276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:52.025542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:39.688002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:40.622646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:41.576128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:42.507179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:43.512221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:44.468433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:45.460777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:46.525175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:47.529152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:48.622564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:49.677267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:50.902026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:52.116417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:39.759065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:40.691343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:41.643994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:42.579496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:43.592170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:44.536631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:45.540549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:46.608076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:47.613896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:48.687510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:49.743908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:59:50.999848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-08T01:59:56.189364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
populationpopulation_malepopulation_femalepopulation_densitypopulation_age_00_09population_age_10_19population_age_20_29population_age_30_39population_age_40_49population_age_50_59population_age_60_69population_age_70_79population_age_80_and_older
population1.0001.0000.9990.2730.9920.9940.9910.9970.9970.9970.9930.9870.979
population_male1.0001.0000.9980.2700.9920.9940.9920.9980.9970.9960.9920.9860.978
population_female0.9990.9981.0000.2760.9920.9940.9900.9960.9960.9970.9930.9880.980
population_density0.2730.2700.2761.0000.3120.2700.4180.3290.2480.2190.1810.2210.212
population_age_00_090.9920.9920.9920.3121.0000.9970.9880.9930.9880.9840.9760.9660.958
population_age_10_190.9940.9940.9940.2700.9971.0000.9920.9930.9900.9860.9800.9700.962
population_age_20_290.9910.9920.9900.4180.9880.9921.0000.9920.9860.9810.9730.9640.957
population_age_30_390.9970.9980.9960.3290.9930.9930.9921.0000.9960.9920.9840.9760.968
population_age_40_490.9970.9970.9960.2480.9880.9900.9860.9961.0000.9960.9890.9830.974
population_age_50_590.9970.9960.9970.2190.9840.9860.9810.9920.9961.0000.9960.9910.984
population_age_60_690.9930.9920.9930.1810.9760.9800.9730.9840.9890.9961.0000.9960.986
population_age_70_790.9870.9860.9880.2210.9660.9700.9640.9760.9830.9910.9961.0000.991
population_age_80_and_older0.9790.9780.9800.2120.9580.9620.9570.9680.9740.9840.9860.9911.000

Missing values

2023-09-08T01:59:52.240926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-08T01:59:52.433514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-08T01:59:52.626903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

location_keypopulationpopulation_malepopulation_femalepopulation_ruralpopulation_urbanpopulation_largest_citypopulation_clusteredpopulation_densityhuman_development_indexpopulation_age_00_09population_age_10_19population_age_20_29population_age_30_39population_age_40_49population_age_50_59population_age_60_69population_age_70_79population_age_80_and_older
0DE_BB_1205172124.035617.036507.0NaNNaNNaNNaN367.4NaN6029.05183.06646.09776.07690.011604.010152.08681.06363.0
1DE_BB_12052100219.049201.051018.0NaNNaNNaNNaN609.9NaN8542.07657.010979.013671.010652.015833.014258.010758.07869.0
2DE_BB_1205357873.028023.029850.0NaNNaNNaNNaN403.2NaN4652.04702.05977.07066.06405.09325.09042.06049.04655.0
3DE_BB_12054178089.086179.091910.0NaNNaNNaNNaN1034.5NaN18893.015636.021671.028980.023226.024768.018998.014762.011155.0
4DE_BB_12060182760.090615.092145.0NaNNaNNaNNaN126.8NaN16693.015671.012966.023038.022973.033665.027041.018553.012160.0
5DE_BB_12061169067.083943.085124.0NaNNaNNaNNaN76.2NaN15377.014196.012369.021416.021413.030742.023868.017171.012515.0
6DE_BB_12062102638.050832.051806.0NaNNaNNaNNaN54.7NaN7800.08044.06330.011463.012143.018962.017256.011843.08797.0
7DE_BB_12063161909.080121.081788.0NaNNaNNaNNaN94.6NaN14768.015191.012267.019401.020906.031071.022170.015921.010214.0
8DE_BB_12064194328.096483.097845.0NaNNaNNaNNaN91.0NaN17410.016338.012100.024176.024303.037375.029789.018637.014200.0
9DE_BB_12065211249.0104111.0107138.0NaNNaNNaNNaN118.8NaN19098.019465.015309.025857.027267.040053.029483.020516.014201.0
location_keypopulationpopulation_malepopulation_femalepopulation_ruralpopulation_urbanpopulation_largest_citypopulation_clusteredpopulation_densityhuman_development_indexpopulation_age_00_09population_age_10_19population_age_20_29population_age_30_39population_age_40_49population_age_50_59population_age_60_69population_age_70_79population_age_80_and_older
5087US_WY_560272392.01099.01293.0NaNNaNNaNNaNNaNNaN244.0228.0291.0319.0285.0294.0380.0208.0143.0
5088US_WY_5602929194.014585.014609.0NaNNaNNaNNaNNaNNaN3318.03471.03092.03386.02987.04000.04599.02824.01517.0
5089US_WY_560318541.04318.04223.0NaNNaNNaNNaNNaNNaN939.0936.0788.0903.0894.01283.01328.0945.0525.0
5090US_WY_5603330147.015145.015002.0NaNNaNNaNNaNNaNNaN3505.03727.03349.03548.03507.03951.04649.02529.01382.0
5091US_WY_560359745.05240.04505.0NaNNaNNaNNaNNaNNaN1233.01165.0954.01270.01244.01356.01330.0832.0361.0
5092US_WY_5603743464.022438.021026.0NaNNaNNaNNaNNaNNaN6334.06333.05488.06734.05219.05534.04815.02063.0944.0
5093US_WY_5603923384.012133.011251.0NaNNaNNaNNaNNaNNaN2461.02245.03184.04184.03404.02968.02855.01435.0648.0
5094US_WY_5604120431.010339.010092.0NaNNaNNaNNaNNaNNaN3282.03182.02179.02755.02349.02567.02496.01116.0505.0
5095US_WY_560438010.04055.03955.0NaNNaNNaNNaNNaNNaN913.01162.0705.0938.0902.01101.01106.0752.0431.0
5096US_WY_560456968.03660.03308.0NaNNaNNaNNaNNaNNaN777.0789.0728.0932.0741.01009.01068.0541.0383.0